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Genome-wide association analysis of imputed rare variants: application to seven common complex diseases.

Mägi R, Asimit JL, Day-Williams AG, Zeggini E, Morris AP - Genet. Epidemiol. (2012)

Bottom Line: Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits.However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants.The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.

View Article: PubMed Central - PubMed

Affiliation: Estonian Genome Centre, University of Tartu, Tartu, Estonia.

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Power, at a nominal significance level of P < 0.05, to detect association of an accumulation of minor alleles with a quantitative trait, for different strategies for assaying rare genetic variation in a 50 kb gene, as a function of the size of the reference panel. Multiple causal variants in the gene contribute jointly to 5% of the overall trait variation. The panels correspond to two specific trait association models: (A) the maximum MAF of any individual causal variant is 1%, and the total MAF of all causal variants is 5%; and (B) the maximum MAF of any individual causal variant is 0.5%, and the total MAF of all causal variants is 2%.
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fig01: Power, at a nominal significance level of P < 0.05, to detect association of an accumulation of minor alleles with a quantitative trait, for different strategies for assaying rare genetic variation in a 50 kb gene, as a function of the size of the reference panel. Multiple causal variants in the gene contribute jointly to 5% of the overall trait variation. The panels correspond to two specific trait association models: (A) the maximum MAF of any individual causal variant is 1%, and the total MAF of all causal variants is 5%; and (B) the maximum MAF of any individual causal variant is 0.5%, and the total MAF of all causal variants is 2%.

Mentions: Figure 1 presents the power, at a nominal significance level of P < 0.05, to detect association with a quantitative trait, for each of the design strategies for assaying rare genetic variation in the gene. For these results, we assumed that multiple rare causal variants in the gene jointly contribute to 5% of the overall trait variation. The panels correspond to two specific trait association models: (A) the maximum MAF of any individual causal variant is 1%, and the total MAF of all causal variants is 5%; and (B) the maximum MAF of any individual causal variant is 0.5%, and the total MAF of all causal variants is 2%. Under the second of these models, we expect fewer rare variants within the gene to be causal, since the total MAF is lower. A higher proportion of non-causal variants within the gene would be expected to reduce power overall, irrespective of the design strategy and/or the number of individuals in the reference panel [Morris and Zeggini, 2010.


Genome-wide association analysis of imputed rare variants: application to seven common complex diseases.

Mägi R, Asimit JL, Day-Williams AG, Zeggini E, Morris AP - Genet. Epidemiol. (2012)

Power, at a nominal significance level of P < 0.05, to detect association of an accumulation of minor alleles with a quantitative trait, for different strategies for assaying rare genetic variation in a 50 kb gene, as a function of the size of the reference panel. Multiple causal variants in the gene contribute jointly to 5% of the overall trait variation. The panels correspond to two specific trait association models: (A) the maximum MAF of any individual causal variant is 1%, and the total MAF of all causal variants is 5%; and (B) the maximum MAF of any individual causal variant is 0.5%, and the total MAF of all causal variants is 2%.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3569874&req=5

fig01: Power, at a nominal significance level of P < 0.05, to detect association of an accumulation of minor alleles with a quantitative trait, for different strategies for assaying rare genetic variation in a 50 kb gene, as a function of the size of the reference panel. Multiple causal variants in the gene contribute jointly to 5% of the overall trait variation. The panels correspond to two specific trait association models: (A) the maximum MAF of any individual causal variant is 1%, and the total MAF of all causal variants is 5%; and (B) the maximum MAF of any individual causal variant is 0.5%, and the total MAF of all causal variants is 2%.
Mentions: Figure 1 presents the power, at a nominal significance level of P < 0.05, to detect association with a quantitative trait, for each of the design strategies for assaying rare genetic variation in the gene. For these results, we assumed that multiple rare causal variants in the gene jointly contribute to 5% of the overall trait variation. The panels correspond to two specific trait association models: (A) the maximum MAF of any individual causal variant is 1%, and the total MAF of all causal variants is 5%; and (B) the maximum MAF of any individual causal variant is 0.5%, and the total MAF of all causal variants is 2%. Under the second of these models, we expect fewer rare variants within the gene to be causal, since the total MAF is lower. A higher proportion of non-causal variants within the gene would be expected to reduce power overall, irrespective of the design strategy and/or the number of individuals in the reference panel [Morris and Zeggini, 2010.

Bottom Line: Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits.However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants.The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.

View Article: PubMed Central - PubMed

Affiliation: Estonian Genome Centre, University of Tartu, Tartu, Estonia.

Show MeSH
Related in: MedlinePlus